code: def bench_params(
      count: int = 1_000_000,
      connectivities: int = range(10, 20),
      dimensions: List[int] = [
          2,
          3,
          4,
          8,
          16,
          32,
          96,
          100,
          256,
          384,
          512,
          768,
          1024,
          1536,
      ],
      expansion_add: int = DEFAULT_EXPANSION_ADD,
      expansion_search: int = DEFAULT_EXPANSION_SEARCH,
  ) -> pd.DataFrame:
      """Measures indexing speed for different dimensionality vectors.
  
      :param count: Number of vectors, defaults to 1_000_000
      :type count: int, optional
      """
  
      results = []
      for connectivity, ndim in itertools.product(
          connectivities, dimensions
      ):
          task = AddTask(
              keys=np.arange(count, dtype=Key),
              vectors=np.random.rand(count, ndim).astype(np.float32),
          )
          index = Index(
              ndim=ndim,
              connectivity=connectivity,
              expansion_add=expansion_add,
              expansion_search=expansion_search,
          )
          result = asdict(task(index))
          result["ndim"] = dimensions
          result["connectivity"] = connectivity
          results.append(result)
  
      # return self._execute_tasks(
      #     tasks,
      #     title='HNSW Indexing Speed vs Vector Dimensions',
      #     x='ndim', y='add_per_second', log_x=True,
      # )
      return pd.DataFrame(results)
  
file_path: usearch/python/scripts/bench.py
imports: import itertools
  from typing import List
  from dataclasses import asdict
  import numpy
  import pandas
  from usearch.index import Index, Key, MetricKind, ScalarKind
  from usearch.numba import jit
  from usearch.eval import Evaluation, AddTask
  from usearch.index import DEFAULT_CONNECTIVITY, DEFAULT_EXPANSION_ADD, DEFAULT_EXPANSION_SEARCH
label_desc: Use image node_id 2 for calling a global function with example usage: bench_params(count, connectivities, dimensions, expansion_add, expansion_search) and returns: pd